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Safety-04 Calculation

Source: R/safety_04.R
safety_04.Rd

The safety_04 function processes EMS incident data for specific safety and transport criteria, filtering by patient age and incident type to identify cases that meet specified exclusion or inclusion criteria. This function accommodates data with various EMS-specific codes, age descriptors, and procedure identifiers.

Usage

safety_04(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  injury_table = NULL,
  procedures_table = NULL,
  disposition_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  earrest_01_col,
  einjury_03_col,
  eprocedures_03_col,
  edisposition_14_col,
  transport_disposition_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A data frame or tibble containing EMS data where each row represents an individual observation.

patient_scene_table

A data frame or tibble containing fields from epatient and escene needed for this measure's calculations.

response_table

A data frame or tibble containing fields from eresponse needed for this measure's calculations.

arrest_table

A data frame or tibble containing fields from earrest needed for this measure's calculations.

injury_table

A data frame or tibble containing fields from einjury needed for this measure's calculations.

procedures_table

A data frame or tibble containing fields from eprocedures needed for this measure's calculations.

disposition_table

A data frame or tibble containing fields from edisposition needed for this measure's calculations.

erecord_01_col

The column containing unique record identifiers for each encounter.

incident_date_col

Column that contains the incident date. This defaults to NULL as it is optional in case not available due to PII restrictions.

patient_DOB_col

Column that contains the patient's date of birth. This defaults to NULL as it is optional in case not available due to PII restrictions.

epatient_15_col

Column name indicating the patient age.

epatient_16_col

Column name for the unit of age (e.g., "Years," "Months").

eresponse_05_col

Column containing response transport codes.

earrest_01_col

Column with cardiac arrest status information.

einjury_03_col

Column describing traumatic injuries, expected as a list or text-separated entries.

eprocedures_03_col

Column listing procedures, assumed to contain multiple procedure codes/texts in each cell.

edisposition_14_col

Column for transport dispositions.

transport_disposition_col

Columns for primary and secondary transport dispositions.

confidence_interval

[Experimental] Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

[Experimental]Character. Specifies the method used to calculate confidence intervals. Options are "wilson" (Wilson score interval) and "clopper-pearson" (exact binomial interval). Partial matching is supported, so "w" and "c" can be used as shorthand.

conf.level

[Experimental]Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.

correct

[Experimental]Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.

...

optional additional arguments to pass onto dplyr::summarize.

Value

A data.frame summarizing results for two population groups (All, Adults and Peds) with the following columns:

  • pop: Population type (All, Adults, and Peds).

  • numerator: Count of incidents meeting the measure.

  • denominator: Total count of included incidents.

  • prop: Proportion of incidents meeting the measure.

  • prop_label: Proportion formatted as a percentage with a specified number of decimal places.

  • lower_ci: Lower bound of the confidence interval for prop (if confidence_interval = TRUE).

  • upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Author

Nicolas Foss, Ed.D., MS

Examples


# Synthetic test data
  test_data <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    epatient_15 = c(34, 5, 45, 2, 60),  # Ages
    epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
    eresponse_05 = rep(2205001, 5),
    earrest_01 = rep("No", 5),
    einjury_03 = rep("non-injury", 5),
    edisposition_14 = rep(4214001, 5),
    edisposition_30 = rep(4230001, 5),
    eprocedures_03 = rep("other response", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  safety_04(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    earrest_01_col = earrest_01,
    einjury_03_col = einjury_03,
    edisposition_14_col = edisposition_14,
    transport_disposition_col = edisposition_30,
    eprocedures_03_col = eprocedures_03,
    confidence_interval = TRUE
  )
#> 
#> ── Safety-04 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Safety-04 ──
#> 
#> Running `safety_04_population()`  [Working on 1 of 13 tasks] ●●●───────────────…
#> Running `safety_04_population()`  [Working on 2 of 13 tasks] ●●●●●●────────────…
#> Running `safety_04_population()`  [Working on 3 of 13 tasks] ●●●●●●●●──────────…
#> Running `safety_04_population()`  [Working on 4 of 13 tasks] ●●●●●●●●●●────────…
#> Running `safety_04_population()`  [Working on 5 of 13 tasks] ●●●●●●●●●●●●●─────…
#> Running `safety_04_population()`  [Working on 6 of 13 tasks] ●●●●●●●●●●●●●●●───…
#> Running `safety_04_population()`  [Working on 7 of 13 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `safety_04_population()`  [Working on 8 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 9 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 10 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 11 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 12 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 13 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Safety-04 ──
#> 
#> 
#> ✔ Function completed in 0.2s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 1 × 8
#>   measure   pop   numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>     <chr>     <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Safety-04 Peds          2           2     1 100%          0.198        1